A review of machine learning applications in soccer with an emphasis on injury risk

George Nassis; Evert Verhagen; João Brito; Pedro Figueiredo; Peter Krustrup

doi:10.5114/biolsport.2023.114283

eISSN: 2083-1862
ISSN: 0860-021X

Biology of Sport

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1/2023
vol. 40

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abstract:

Review paper

A review of machine learning applications in soccer with an emphasis on injury risk

George P. Nassis

^{1, 2}

,

Evert Verhagen

³

,

João Brito

⁴

,

Pedro Figueiredo

^{4, 5}

,

Peter Krustrup

^{2, 6, 7}

Physical Education Department, College of Education, United Arab Emirates University, Al Ain, Abu Dhabi, United Arab Emirates
Department of Sports Science and Clinical Biomechanics, SDU Sport and Health Sciences Cluster (SHSC), University of Southern Denmark, Odense, Denmark
Amsterdam Collaboration on Health and Safety in Sports, Department of Public and Occupational Health, Amsterdam UMC, Amsterdam Movement Sciences, Amsterdam, Netherlands
Portugal Football School, Portuguese Football Federation, Oeiras, Portugal
CIDEFES, Universidade Lusófona, Lisboa, Portugal
Danish Institute for Advanced Study (DIAS), University of Southern Denmark, Odense, Denmark
Sport and Health Sciences, College of Life and Environmental Sciences, University of Exeter, United Kingdom

Biol Sport. 2023;40(1):233–239

DOI: https://doi.org/10.5114/biolsport.2023.114283

Online publish date: 2022/03/16

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This narrative review paper aimed to discuss the literature on machine learning applications in soccer with an emphasis on injury risk assessment. A secondary aim was to provide practical tips for the health and performance staff in soccer clubs on how machine learning can provide a competitive advantage. Performance analysis is the area with the majority of research so far. Other domains of soccer science and medicine with machine learning use are injury risk assessment, players’ workload and wellness monitoring, movement analysis, players’ career trajectory, club performance, and match attendance. Regarding injuries, which is a hot topic, machine learning does not seem to have a high predictive ability at the moment (models specificity ranged from 74.2%-97.7%. sensitivity from 15.2%-55.6% with area under the curve of 0.66–0.83). It seems, though, that machine learning can help to identify the early signs of elevated risk for a musculoskeletal injury. Future research should account for musculoskeletal injuries’ dynamic nature for machine learning to provide more meaningful results for practitioners in soccer.

keywords:

A review of machine learning applications in soccer with an emphasis on injury risk

George P. Nassis 1, 2 , Evert Verhagen 3 , João Brito 4 , Pedro Figueiredo 4, 5 , Peter Krustrup 2, 6, 7

Machine learning, Soccer injury risk, Data analytics, Big data, Football

George P. Nassis

^{1, 2}

,

Evert Verhagen

³

,

João Brito

⁴

,

Pedro Figueiredo

^{4, 5}

,

Peter Krustrup

^{2, 6, 7}